Machine Learning Classifier for Electrical Device Failure Region Determination
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Solution Overview
Problem
Conventional methods are inadequate for accurately determining the failure regions of electrical devices due to their inability to handle multiple factors and diffuse pass/fail borders, requiring extensive testing and sampling, which is time-consuming and inefficient.
Innovation Solution
A machine learning classifier is trained using data points from electrical devices to recognize patterns and predict pass/fail states for new combinations of factors, allowing for adaptive sampling to focus on regions near the pass/fail border, thereby improving accuracy and scalability to multi-dimensional spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional testing methods (sweep, binary search, grid search) are used to determine pass/fail border regions, then the analysis can be performed with simple methods, but the methods are only suitable for low number of factors and have bad resolution
Solution Approach 1:
The patent introduces an intermediary statistical model that mediates between the simple testing approach and the need for high resolution. The model fits a continuous function to binary pass/fail data points, creating an intermediate representation that enables precise border determination without requiring dense sampling of the entire factor space.
Solution Approach 2:
The patent performs preliminary action by collecting pass/fail data points at strategically selected factor combinations before applying the statistical model. This preliminary sampling, combined with the model fitting, allows the system to achieve high resolution border determination without exhaustive testing of all possible factor combinations.
2Measurement precision
If more samples are tested to improve the precision of pass/fail border determination, then the resolution increases, but the testing time increases
Solution Approach 1:
The patent applies partial action by testing only a limited number of strategically selected factor combinations rather than exhaustively testing all possible combinations. The statistical model compensates for the limited sampling, enabling accurate border determination with fewer tests than traditional methods would require.
Solution Approach 2:
The patent performs preliminary action by selecting and testing key factor combinations before applying the statistical model. This preliminary sampling at critical points, combined with model interpolation, achieves high precision without requiring continuous exhaustive testing.
3Productivity
If binary search approach is used to find pass/fail border quickly, then the convergence speed improves, but the method only works with one factor and cannot handle multiple factors
Solution Approach 1:
The patent transitions from one-dimensional binary search to multi-dimensional analysis by fitting a statistical model to pass/fail data across multiple factors simultaneously. The model extends the binary search concept to higher dimensions, enabling fast convergence while handling multiple influencing factors through the mathematical framework of the statistical model.
4Measurement precision
If grid search is performed on two or more factors to improve coverage, then the sampling coverage increases, but the number of required samples increases exponentially
Solution Approach 1:
The patent applies partial action by sampling only a limited subset of the factor space rather than performing exhaustive grid search. The statistical model interpolates and extrapolates from these limited samples, achieving comprehensive coverage understanding without requiring exponential numbers of test points that would be necessary for complete grid search.
Solution Approach 2:
The patent performs preliminary action by strategically selecting and testing a limited number of factor combinations before applying the statistical model. This preliminary sampling at key locations, combined with model-based inference, achieves effective coverage of the multi-dimensional factor space without exhaustive testing.
Data Source
AI summary
One or more failure regions are determined for an electrical device by training a machine learning classifier, including analyzing data points for the device and recognizing patterns in the data points. Each data point indicates pass or fail of the device for a particular combination of factors relating to the operation of the device. The trained machine learning classifier is used to predict the pass/fail state of new data points for the electrical device. Each new data point corresponds to a new combination of the factors relating to the operation of the device not previously analyzed by the machine learning classifier. A pass/fail border region can be identified for the electrical device based on the training of the machine learning classifier, the pass/fail border region excluding data points for which the electrical device is expected to pass or fail with a high degree of certainty.